Covid19Japan.com
Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。
Import
Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。
個票データ（Patient Data）
陽性者単位の個票データ。
path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"
df <- path %>%
paste0("latest.json") %>%
readr::read_lines() %>%
paste0(path, .) %>%
jsonlite::fromJSON()
df
集計データ（Summary Data）
死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。
path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"
df_s <- path %>%
paste0("latest.json") %>%
readr::read_lines() %>%
paste0(path, .) %>%
jsonlite::fromJSON()
df_s %>% summary()## Length Class Mode
## prefectures 27 data.frame list
## regions 12 data.frame list
## daily 37 data.frame list
## updated 1 -none- character
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。
都道府県単位集計
更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。
df_s$prefectures陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。
| 項目 | 内容 | 備考 |
|---|---|---|
| dailyConfirmedCount | 陽性者数 | 単日 |
| dailyConfirmedStartDate | 陽性者数のカウント開始日 | 区分により開始日が異なる |
| dailyDeceasedCount | 死亡者数 | 単日 |
| dailyDeceasedStartDate | 死亡者数のカウント開始日 | 区分により開始日が異なる |
| dailyRecoveredCumulative | 快復者数 | 累計 |
| dailyRecoveredStartDate | 快復者数のカウント開始日 | 区分により開始日が異なる |
| dailyActive | 治療者数1 | 単日 |
| dailyActiveStartDate | 治療者数のカウント開始日 | 区分により開始日が異なる |
1 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている
地方単位集計
更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。
df_s$regionsdf_s$regions$confirmed[1]## [1] 69889df_s$regions$dailyConfirmedCount[[1]] %>% sum()## [1] 76641
日次集計
個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。
df_s$daily
更新日時
集計データの更新日時。
df_s$updated## [1] "2020-11-27T21:13:21+09:00"
Others
病床データ
新型コロナウイルス対策病床オープンデータのデータも用意しておく。
if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>%
googlesheets4::read_sheet() %>%
dplyr::arrange(dplyr::desc(`発表日`)) %>%
dplyr::distinct(`自治体名`, .keep_all = TRUE) %>%
dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
date = `発表日`) %>%
dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}
対策ダッシュボード
NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。
"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>%
readr::read_csv(guess_max = 12500) %>%
dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
`入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
`確認中`) %>%
dplyr::mutate(date = lubridate::as_date(date)) %>%
dplyr::mutate_if(is.numeric, .funs = as.integer)
新型コロナ関連ニュース
新型コロナ関連のニュース
news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>%
readr::read_csv() %>%
dplyr::filter(area == "日本")
news
Summarize
最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。
df %>%
skimr::skim()| Name | Piped data |
| Number of rows | 144658 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| character | 19 |
| logical | 3 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| patientId | 0 | 1.00 | 1 | 8 | 0 | 142750 | 0 |
| dateAnnounced | 0 | 1.00 | 10 | 10 | 0 | 304 | 0 |
| gender | 40051 | 0.72 | 1 | 1 | 0 | 2 | 0 |
| detectedPrefecture | 0 | 1.00 | 3 | 15 | 0 | 49 | 0 |
| patientStatus | 140468 | 0.03 | 8 | 23 | 0 | 8 | 0 |
| notes | 78318 | 0.46 | 1 | 270 | 0 | 63396 | 1 |
| mhlwPatientNumber | 144209 | 0.00 | 1 | 11 | 0 | 434 | 0 |
| prefecturePatientNumber | 35981 | 0.75 | 5 | 20 | 0 | 108668 | 0 |
| prefectureSourceURL | 113266 | 0.22 | 5 | 224 | 0 | 3454 | 0 |
| residence | 48286 | 0.67 | 1 | 38 | 0 | 1429 | 0 |
| sourceURL | 1183 | 0.99 | 1 | 239 | 0 | 8859 | 0 |
| relatedPatients | 132779 | 0.08 | 2 | 259 | 0 | 7087 | 0 |
| knownCluster | 142126 | 0.02 | 3 | 88 | 0 | 235 | 0 |
| detectedCityTown | 116521 | 0.19 | 2 | 22 | 0 | 667 | 0 |
| cityPrefectureNumber | 116822 | 0.19 | 1 | 34 | 0 | 27827 | 2 |
| citySourceURL | 132546 | 0.08 | 9 | 317 | 0 | 3686 | 0 |
| deceasedDate | 142627 | 0.01 | 10 | 10 | 0 | 254 | 0 |
| deceasedReportedDate | 143438 | 0.01 | 10 | 62 | 0 | 207 | 0 |
| deathSourceURL | 143583 | 0.01 | 14 | 123 | 0 | 656 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| confirmedPatient | 0 | 1 | 0.99 | TRU: 142749, FAL: 1909 |
| charterFlightPassenger | 144644 | 0 | 1.00 | TRU: 14 |
| cruisePassengerDisembarked | 144647 | 0 | 1.00 | TRU: 11 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ageBracket | 0 | 1 | 27.19 | 24.8 | -1 | -1 | 20 | 40 | 100 | ▇▇▅▂▁ |
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。
Tidy & Transform
各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。
x <- df %>%
dplyr::select(patientId, date = dateAnnounced, gender,
pref = detectedPrefecture, patientStatus, knownCluster,
confirmedPatient, charterFlightPassenger,
cruisePassengerDisembarked, ageBracket,
deceasedDate, deceasedReportedDate) %>%
dplyr::filter(confirmedPatient == TRUE) %>%
dplyr::mutate(date = lubridate::as_date(date),
gender = forcats::as_factor(gender),
patientStatus = forcats::as_factor(patientStatus),
cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
ageBracket = forcats::as_factor(ageBracket),
deceasedDate = lubridate::as_date(deceasedDate),
deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
dplyr::select(-`推計人口`, -pref) %>%
dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>%
tidyr::drop_na(pref)
x変換結果を要約してみると
x %>%
skimr::skim()| Name | Piped data |
| Number of rows | 141249 |
| Number of columns | 18 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| Date | 3 |
| factor | 9 |
| logical | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| patientId | 0 | 1.00 | 2 | 8 | 0 | 141249 | 0 |
| knownCluster | 138767 | 0.02 | 3 | 88 | 0 | 232 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| date | 0 | 1 | 2020-01-15 | 2020-11-27 | 2020-09-05 | 301 |
| deceasedDate | 140870 | 0 | 2020-02-13 | 2020-11-19 | 2020-05-08 | 150 |
| deceasedReportedDate | 140920 | 0 | 2020-02-13 | 2020-10-17 | 2020-05-16 | 130 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| gender | 38234 | 0.73 | FALSE | 2 | M: 57690, F: 45325 |
| patientStatus | 138738 | 0.02 | FALSE | 8 | Hos: 1246, Dec: 371, Hom: 315, Dis: 276 |
| ageBracket | 0 | 1.00 | FALSE | 13 | -1: 38319, 20: 27807, 30: 17853, 40: 14986 |
| pcode | 0 | 1.00 | FALSE | 47 | 13: 39698, 27: 19168, 14: 12132, 23: 9644 |
| pref | 0 | 1.00 | FALSE | 47 | 東京都: 39698, 大阪府: 19168, 神奈川: 12132, 愛知県: 9644 |
| region | 0 | 1.00 | FALSE | 8 | 関東地: 69889, 近畿地: 30177, 中部地: 15084, 九州地: 12793 |
| 広域圏 | 12438 | 0.91 | FALSE | 8 | 首都圏: 70226, 近畿圏: 29381, 中部圏: 13602, 九州圏: 8618 |
| 通俗的区分 | 0 | 1.00 | FALSE | 11 | 関東: 69889, 関西: 29381, 東海: 12933, 九州: 8618 |
| fct_pref | 0 | 1.00 | FALSE | 47 | Tok: 39698, Osa: 19168, Kan: 12132, Aic: 9644 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| confirmedPatient | 0 | 1 | 1.00 | TRU: 141249 |
| charterFlightPassenger | 141242 | 0 | 1.00 | TRU: 7 |
| cruisePassengerDisembarked | 141238 | 0 | 1.00 | TRU: 11 |
| cluster | 0 | 1 | 0.02 | FAL: 138767, TRU: 2482 |
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば
- 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
- 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
- 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い
ことが読める。
patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。
x %>%
dplyr::group_by(patientStatus) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
"重症", "自宅療養", "ホテル療養", NA))
Data Wrangling
陽性者の集計
最初に陽性者をキーに集計する。
全国集計
全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。
r_by_all <- x %>%
dplyr::filter(!is.na(pref)) %>%
dplyr::summarise(n = n()) %>%
dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_all %>%
dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate)
地方別集計
次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。
region <- prefs %>%
dplyr::group_by(`八地方区分`) %>%
dplyr::summarise(population = sum(`推計人口`)) %>%
dplyr::rename(region = `八地方区分`)
r_by_region <- x %>%
dplyr::group_by(region) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
dplyr::left_join(region, by = c("region" = "region")) %>%
dplyr::select(region, n, population) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_region %>%
dplyr::rename(`地方` = region,
`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate)
上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.12）。
r_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
都道府県別集計
同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。
r_by_pref <- x %>%
dplyr::group_by(pref) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>%
dplyr::select(pref, n, population = `推計人口`) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_pref %>%
dplyr::rename(`都道府県` = pref,
`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate) %>%
tibble::rowid_to_column("No") %>%
DT::datatable()
上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.12）。
r_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。
r_by_pref %>%
dplyr::filter(population < 5500) %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
クラスタ比率
全国のクラスタ比率
x %>%
dplyr::filter(!is.na(pref)) %>%
dplyr::group_by(cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio)地方別クラスタ比率
地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。
x %>%
dplyr::group_by(region, cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
dplyr::rename(`地方` = region,
`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio)
都道府県別クラスタ比率
同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。
x %>%
dplyr::group_by(pref, cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>%
dplyr::rename(`都道府県` = pref,
`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio) %>%
tibble::rowid_to_column(var = "No") %>%
DT::datatable()
陽性者の日次集計
全国日次集計
全国の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_all <- x %>%
dplyr::group_by(date) %>%
dplyr::summarise(n = n()) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
fill = list(n = 0L)) %>%
dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))
x_by_all %>%
dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
`累計陽性者数` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"
# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")
sec_scale <- 100
emergency <- news %>%
dplyr::filter(category == "緊急事態")
goto <- news %>%
dplyr::filter(category == "GoTo")
x_by_all %>%
dplyr::mutate(hday = is.jholiday(target_date = date,
holiday_source = "./Covid19/syukujitsu.csv"),
wday = lubridate::wday(date, week_start = 1),
wday = dplyr::if_else(wday > 5, TRUE, FALSE),
wday = dplyr::if_else(hday | wday, 3000L, NA_integer_)) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
fill = "red", alpha = 0.1) +
ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
colour = "dark blue", linetype = "dashed", size = 0.15) +
ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
colour = "magenta", linetype = "dashed", size = 0.25) +
ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
fill = "dark gray", alpha = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
colour = "dark green", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
colour = "dark green", size = 1.0) +
ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
size = 2.0, data = emergency) +
ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
size = 2.5, data = goto, colour = "magenta") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
x_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) +
ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "前日差")
地方別日次集計
同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_region <- x %>%
dplyr::group_by(date, region) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>%
tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>%
tidyr::replace_na(replace = list(n = 0L)) %>%
dplyr::group_by(region) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n)),
ma28 = purrr::map(data, ~ ma28(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
by = c("八地方区分" = "region")) %>%
dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>%
dplyr::arrange(date)
x_by_region %>%
dplyr::filter(date == max(date)) %>%
dplyr::mutate(ma7 = round(ma7, 1)) %>%
dplyr::select(`地方` = region,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)x_by_region %>%
dplyr::select(`地方` = region,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = n)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
width = 1.0, alpha = 0.5) +
ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "陽性者数") 
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) +
ggplot2::geom_line(size = 1) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "陽性者数") +
ggrepel::geom_text_repel(ggplot2::aes(label = region),
data = subset(x_by_region, date == max(date)),
nudge_x = 30, segment.alpha = 0.5, size = 4) +
ggplot2::lims(x = c(min(x_by_region$date),
max(x_by_region$date) + 45))
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) +
ggplot2::geom_line(size = 1) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "累計陽性者数") +
ggrepel::geom_text_repel(ggplot2::aes(label = region),
data = subset(x_by_region, date == max(date)),
nudge_x = 30, segment.alpha = 0.5, size = 4) +
ggplot2::lims(x = c(min(x_by_region$date),
max(x_by_region$date) + 45))
地方単位で可視化。
sec_scale <- 20
ncol <- 2
x_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.5, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "dotted", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（点線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "陽性者累計（実線）")
)
傾向が見えるように縦軸をフリースケールとする。
sec_scale <- 20
ncol <- 2
x_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.5, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "dashed", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "陽性者累計（実線）")
)
都道府県別日次集計
同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_pref <- x %>%
dplyr::group_by(date, pref) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>%
tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>%
tidyr::replace_na(replace = list(n = 0L)) %>%
dplyr::group_by(pref) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n)),
ma28 = purrr::map(data, ~ ma28(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>%
dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>%
dplyr::arrange(date)
x_by_pref %>%
dplyr::filter(date == max(date)) %>%
dplyr::mutate(ma7 = round(ma7, 1)) %>%
dplyr::select(`都道府県` = pref,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7) %>%
DT::datatable()x_by_pref %>%
dplyr::select(`都道府県` = pref,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
x_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
傾向が見えるように縦軸をフリースケールとする。
x_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
死亡者の日次集計
厚生労働省のデータと乖離がある。
都道府県別
都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。
start <- df_s$prefectures %>%
dplyr::select(pref = name, date = dailyDeceasedStartDate) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
dplyr::arrange(pcode) %>%
tidyr::drop_na(pcode) %>%
dplyr::select(date, pref = `都道府県`) %>%
dplyr::distinct(date) %>%
.$date %>% lubridate::as_date()
d_by_prefs <- df_s$prefectures %>%
dplyr::select(deceased = dailyDeceasedCount, pref = name) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
tidyr::drop_na(pcode) %>%
dplyr::select(pref = `都道府県`, deceased) %>%
tidyr::unnest(deceased) %>%
tidyr::pivot_wider(names_from = pref, values_from = deceased) %>%
tidyr::unnest() %>%
dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
by = "day")) %>%
dplyr::select(date, dplyr::everything()) %>%
tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>%
dplyr::group_by(pref) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>%
dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>%
dplyr::select(date, pref, n, diff, cum, ma7) %>%
dplyr::arrange(date)
d_by_prefssec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_prefs %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
地方別
集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。
d_by_region <- d_by_prefs %>%
dplyr::select(date, pref = pref, n) %>%
dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>%
tidyr::drop_na(pcode) %>%
dplyr::group_by(date, `八地方区分`) %>%
dplyr::summarise(n = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::rename(region = `八地方区分`) %>%
dplyr::group_by(region) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n))) %>%
tidyr::unnest() %>%
dplyr::arrange(date)
d_by_region
陽性者比率と死亡者比率
rpd_by_all <- d_by_region %>%
dplyr::group_by(region) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_region, ., by = c("region")) %>%
dplyr::select(region, positive = n, deceased = d, population) %>%
dplyr::select(-region) %>%
dplyr::summarise_all(sum) %>%
dplyr::mutate(p_rate = round(positive / population, 2),
d_rate = round(deceased / positive, 2))
rpd_by_all %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
rpd_by_region <- d_by_region %>%
dplyr::group_by(region) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_region, ., by = c("region")) %>%
dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>%
dplyr::mutate(d_rate = round(deceased / positive, 2))
rpd_by_region %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
rpd_by_prefs <- d_by_prefs %>%
dplyr::group_by(pref) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_pref, ., by = "pref") %>%
dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>%
dplyr::mutate(d_rate = round(deceased / positive, 2))
rpd_by_prefs %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
全国日次集計
都道府県別のデータから全国の日次集計を求める。
d_by_all <- d_by_prefs %>%
dplyr::group_by(date) %>%
dplyr::summarise(n = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_allsec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
linetype = "dashed", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・同移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計死亡者数（実線）")
)
年代別
x_by_age <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9"))
x_by_agex %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::group_by(ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
dplyr::mutate(rate = round((n / sum(n) * 100), 1))
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
tidyr::pivot_wider(names_from = ageBracket, values_from = n)x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::group_by(region, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::facet_grid(~ cluster) +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(region, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::facet_grid(~ cluster) +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
都道府県別
g_age_by_pref <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9")) %>%
dplyr::group_by(pref, ageBracket, cluster) %>%
# dplyr::group_by(pref, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(pref = forcats::fct_rev(pref)) %>%
ggplot2::ggplot(ggplot2::aes(x = pref)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
# ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
colour = "dark gray") +
ggplot2::labs(title = paste0("年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
g_age_by_pref
g_age_by_pref +
ggplot2::facet_grid(~ cluster)
g_age_by_pref_wo <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(pref, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(pref = forcats::fct_rev(pref)) %>%
ggplot2::ggplot(ggplot2::aes(x = pref)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
colour = "dark gray") +
ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
g_age_by_pref_wo
g_age_by_pref_wo +
ggplot2::facet_grid(~ cluster) +
ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))
Visualize
前日差
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
ggplot2::facet_wrap(~ region, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
caption = caption, x = "", y = "")
都道府県別
単日＋累計
sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
前日差
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
x = "", y = "")
死亡者の日次推移
全国
sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
alpha = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
colour = "dark green", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
colour = "dark green", size = 1.0) +
ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) +
ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "前日差")
地方別
sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
linetype = "solid", size = 0.2) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
ggplot2::facet_wrap(~ region, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
linetype = "solid", size = 0.2) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
都道府県別日次推移
sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数（単日）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数（単日）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
比較
陽性者数と死亡者の比較。
全国
sec_scale <- (1 / 50)
x_by_all %>%
dplyr::left_join(d_by_all, by = c("date")) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
fill = "dark green", alpha = 0.25, width = 1.0) +
ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
fill = "dark red", alpha = 0.25, width = 1.0) +
# ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") +
# ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") +
ggplot2::labs(title = paste0("@", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数（濃緑）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "死亡者数（濃赤）")
)
地方別
sec_scale <- (1 / 10)
ncol <- 4
x_by_region %>%
dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
fill = "dark green", alpha = 0.25, width = 1.0) +
ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
fill = "dark red", alpha = 0.25, width = 1.0) +
# ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") +
# ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") +
ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数（濃緑）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "死亡者数（濃赤）")
)
相関
地方区分別
r_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = region)) +
ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
rpd_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_point(ggplot2::aes(colour = region)) +
ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
都道府県別
r_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("@", datetime), caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
r_by_pref %>%
dplyr::filter(n < 5000) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
rpd_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
rpd_by_prefs %>%
dplyr::filter(positive < 1000) %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
Model
時系列（TS）分析
日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。
ts_week <- x_by_all %>%
dplyr::select(n) %>%
ts(frequency = 7)時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。
ts_week %>%
plot(main = paste0("全国 @", datetime))
上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。
ts_week %>%
base::diff() %>%
plot(main = paste0("全国 @", datetime))
トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。
ts_week %>%
stats::decompose() %>%
plot()
トレンドを抜き出してみる。移動平均に酷似している。
ts_week %>%
stats::decompose() %>%
.$x %>%
plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))
par(new = TRUE)
ts_week %>%
stats::decompose() %>%
.$trend %>%
plot(ylim = c(0, 1500), col = "dark green", lwd = 3)
地方別時系列分析
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道地方
## NULL
##
## $東北地方
## NULL
##
## $関東地方
## NULL
##
## $中部地方
## NULL
##
## $近畿地方
## NULL
##
## $中国地方
## NULL
##
## $四国地方
## NULL
##
## $九州地方
## NULLoldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, .y) {
plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
par(new = TRUE)
stats::decompose(.x) %>%
.$trend %>%
plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
} )
## $北海道地方
## NULL
##
## $東北地方
## NULL
##
## $関東地方
## NULL
##
## $中部地方
## NULL
##
## $近畿地方
## NULL
##
## $中国地方
## NULL
##
## $四国地方
## NULL
##
## $九州地方
## NULLpar(oldpar)x_by_pref %>%
dplyr::select(pref, n) %>%
split(.$pref) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, .y) {
plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
# plot(.x, main = region)
par(new = TRUE)
stats::decompose(.x) %>%
.$trend %>%
plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
} )
## $北海道
## NULL
##
## $青森県
## NULL
##
## $岩手県
## NULL
##
## $宮城県
## NULL
##
## $秋田県
## NULL
##
## $山形県
## NULL
##
## $福島県
## NULL
##
## $茨城県
## NULL
##
## $栃木県
## NULL
##
## $群馬県
## NULL
##
## $埼玉県
## NULL
##
## $千葉県
## NULL
##
## $東京都
## NULL
##
## $神奈川県
## NULL
##
## $新潟県
## NULL
##
## $富山県
## NULL
##
## $石川県
## NULL
##
## $福井県
## NULL
##
## $山梨県
## NULL
##
## $長野県
## NULL
##
## $岐阜県
## NULL
##
## $静岡県
## NULL
##
## $愛知県
## NULL
##
## $三重県
## NULL
##
## $滋賀県
## NULL
##
## $京都府
## NULL
##
## $大阪府
## NULL
##
## $兵庫県
## NULL
##
## $奈良県
## NULL
##
## $和歌山県
## NULL
##
## $鳥取県
## NULL
##
## $島根県
## NULL
##
## $岡山県
## NULL
##
## $広島県
## NULL
##
## $山口県
## NULL
##
## $徳島県
## NULL
##
## $香川県
## NULL
##
## $愛媛県
## NULL
##
## $高知県
## NULL
##
## $福岡県
## NULL
##
## $佐賀県
## NULL
##
## $長崎県
## NULL
##
## $熊本県
## NULL
##
## $大分県
## NULL
##
## $宮崎県
## NULL
##
## $鹿児島県
## NULL
##
## $沖縄県
## NULL
Infer
時系列予測（ARIMA）
ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。
全国
x_by_all %>%
dplyr::select(n) %>%
ts(.$n, frequency = 7) %>%
forecast::auto.arima() %>%
forecast::forecast() %>%
plot(main = paste0("全国 @", datetime))
地方別
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map(., forecast::auto.arima) %>%
purrr::map(., forecast::forecast) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 261.6899 250.2059 249.4073 238.1762 230.4874 253.6377 256.1481 268.7122
## [9] 267.4404 270.4514 269.1841 273.7080 276.4356 282.1470
##
## $北海道地方$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 248.9786 242.2496
## 46.57143 235.3801 227.5317
## 46.71429 230.7230 220.8321
## 46.85714 218.4233 207.9668
## 47.00000 209.3558 198.1694
## 47.14286 231.7314 220.1349
## 47.28571 232.8265 220.4808
## 47.42857 242.1157 228.0363
## 47.57143 238.1574 222.6559
## 47.71429 238.3323 221.3295
## 47.85714 234.9911 216.8905
## 48.00000 237.5613 218.4264
## 48.14286 238.5081 218.4305
## 48.28571 242.2534 221.1350
##
## $北海道地方$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 274.4013 281.1303
## 46.57143 265.0318 272.8801
## 46.71429 268.0915 277.9824
## 46.85714 257.9290 268.3856
## 47.00000 251.6190 262.8054
## 47.14286 275.5441 287.1406
## 47.28571 279.4698 291.8155
## 47.42857 295.3088 309.3881
## 47.57143 296.7233 312.2248
## 47.71429 302.5705 319.5733
## 47.85714 303.3770 321.4777
## 48.00000 309.8547 328.9896
## 48.14286 314.3630 334.4406
## 48.28571 322.0406 343.1590
##
##
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 35.06038 33.64008 31.93289 29.59013 32.86287 35.30819 36.77091 36.35933
## [9] 35.87403 34.43652 33.37128 33.13064 34.09146 35.28233
##
## $東北地方$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 28.23942 24.62862
## 46.57143 26.52315 22.75568
## 46.71429 24.73833 20.92976
## 46.85714 22.39532 18.58661
## 47.00000 25.51956 21.63225
## 47.14286 27.94655 24.04953
## 47.28571 29.27409 25.30551
## 47.42857 28.58369 24.46752
## 47.57143 27.63229 23.26938
## 47.71429 25.93065 21.42791
## 47.85714 24.70067 20.11072
## 48.00000 24.37889 19.74599
## 48.14286 25.27424 20.60668
## 48.28571 26.39915 21.69668
##
## $東北地方$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 41.88135 45.49215
## 46.57143 40.75701 44.52449
## 46.71429 39.12745 42.93602
## 46.85714 36.78494 40.59364
## 47.00000 40.20617 44.09348
## 47.14286 42.66984 46.56686
## 47.28571 44.26773 48.23630
## 47.42857 44.13497 48.25115
## 47.57143 44.11577 48.47868
## 47.71429 42.94240 47.44514
## 47.85714 42.04189 46.63184
## 48.00000 41.88240 46.51529
## 48.14286 42.90869 47.57625
## 48.28571 44.16551 48.86798
##
##
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 1081.1161 837.5324 669.5467 707.6508 923.1024 1074.0062 1069.3304
## [8] 1093.8265 876.2474 723.6673 794.2970 984.0100 1117.6150 1108.1634
##
## $関東地方$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 1004.8823 964.5266
## 46.57143 741.0216 689.9319
## 46.71429 563.3103 507.0721
## 46.85714 596.8775 538.2376
## 47.00000 808.1202 747.2523
## 47.14286 955.1033 892.1599
## 47.28571 946.7626 881.8791
## 47.42857 957.7620 885.7339
## 47.57143 730.6245 653.5364
## 47.71429 571.0731 490.2946
## 47.85714 636.5481 553.0409
## 48.00000 821.4414 735.3828
## 48.14286 950.5267 862.0756
## 48.28571 936.8263 846.1259
##
## $関東地方$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 1157.3499 1197.7056
## 46.57143 934.0433 985.1330
## 46.71429 775.7832 832.0213
## 46.85714 818.4242 877.0640
## 47.00000 1038.0845 1098.9524
## 47.14286 1192.9091 1255.8525
## 47.28571 1191.8982 1256.7817
## 47.42857 1229.8909 1301.9190
## 47.57143 1021.8703 1098.9584
## 47.71429 876.2615 957.0400
## 47.85714 952.0458 1035.5530
## 48.00000 1146.5786 1232.6372
## 48.14286 1284.7033 1373.1545
## 48.28571 1279.5005 1370.2009
##
##
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 359.1465 294.0859 253.0731 210.9472 257.1073 275.6116 310.9396 334.3489
## [9] 317.4232 297.6264 259.0631 258.1802 257.7712 276.9191
##
## $中部地方$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 336.0504 323.8241
## 46.57143 265.2462 249.9793
## 46.71429 218.8980 200.8068
## 46.85714 174.1917 154.7346
## 47.00000 218.8650 198.6208
## 47.14286 236.3433 215.5560
## 47.28571 270.4424 249.0045
## 47.42857 287.4624 262.6422
## 47.57143 265.0506 237.3263
## 47.71429 239.5174 208.7563
## 47.85714 196.7666 163.7889
## 48.00000 192.9382 158.4011
## 48.14286 190.4206 154.7674
## 48.28571 207.6768 171.0222
##
## $中部地方$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 382.2425 394.4689
## 46.57143 322.9256 338.1925
## 46.71429 287.2481 305.3393
## 46.85714 247.7026 267.1597
## 47.00000 295.3496 315.5938
## 47.14286 314.8798 335.6672
## 47.28571 351.4367 372.8747
## 47.42857 381.2354 406.0556
## 47.57143 369.7958 397.5202
## 47.71429 355.7354 386.4965
## 47.85714 321.3596 354.3374
## 48.00000 323.4223 357.9593
## 48.14286 325.1217 360.7749
## 48.28571 346.1613 382.8160
##
##
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 638.4681 650.2637 456.1357 493.6186 579.9950 651.9346 648.3123 694.2784
## [9] 710.2649 546.2803 531.2765 614.7672 679.3056 671.0992
##
## $近畿地方$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 594.7387 571.5898
## 46.57143 597.6896 569.8585
## 46.71429 400.7252 371.3926
## 46.85714 435.5098 404.7489
## 47.00000 519.3079 487.1821
## 47.14286 588.7743 555.3393
## 47.28571 582.7721 548.0772
## 47.42857 618.3028 578.0838
## 47.57143 628.0302 584.4978
## 47.71429 460.0703 414.4335
## 47.85714 441.2667 393.6184
## 48.00000 521.1115 471.5332
## 48.14286 582.1408 530.7049
## 48.28571 570.5477 517.3190
##
## $近畿地方$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 682.1975 705.3465
## 46.57143 702.8378 730.6689
## 46.71429 511.5463 540.8789
## 46.85714 551.7273 582.4883
## 47.00000 640.6821 672.8079
## 47.14286 715.0948 748.5299
## 47.28571 713.8524 748.5473
## 47.42857 770.2539 810.4730
## 47.57143 792.4996 836.0320
## 47.71429 632.4903 678.1270
## 47.85714 621.2864 668.9347
## 48.00000 708.4228 758.0011
## 48.14286 776.4704 827.9063
## 48.28571 771.6508 824.8795
##
##
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 34.18543 32.83970 32.83970 32.83970 32.83970 32.83970 32.83970 32.83970
## [9] 32.83970 32.83970 32.83970 32.83970 32.83970 32.83970
##
## $中国地方$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 24.66653 19.62754
## 46.57143 22.70268 17.33646
## 46.71429 22.43887 16.93301
## 46.85714 22.18160 16.53954
## 47.00000 21.93038 16.15534
## 47.14286 21.68483 15.77980
## 47.28571 21.44456 15.41234
## 47.42857 21.20926 15.05248
## 47.57143 20.97863 14.69975
## 47.71429 20.75239 14.35376
## 47.85714 20.53031 14.01412
## 48.00000 20.31217 13.68050
## 48.14286 20.09776 13.35259
## 48.28571 19.88690 13.03011
##
## $中国地方$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 43.70432 48.74331
## 46.57143 42.97671 48.34293
## 46.71429 43.24052 48.74638
## 46.85714 43.49780 49.13985
## 47.00000 43.74901 49.52405
## 47.14286 43.99456 49.89959
## 47.28571 44.23483 50.26705
## 47.42857 44.47013 50.62691
## 47.57143 44.70077 50.97964
## 47.71429 44.92700 51.32564
## 47.85714 45.14908 51.66528
## 48.00000 45.36722 51.99890
## 48.14286 45.58163 52.32680
## 48.28571 45.79249 52.64929
##
##
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 16.62562 17.40069 17.40069 17.40069 17.40069 17.40069 17.40069 17.40069
## [9] 17.40069 17.40069 17.40069 17.40069 17.40069 17.40069
##
## $四国地方$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 12.81684 10.800590
## 46.57143 13.13945 10.883692
## 46.71429 12.91994 10.547968
## 46.85714 12.71068 10.227941
## 47.00000 12.51037 9.921595
## 47.14286 12.31795 9.627312
## 47.28571 12.13255 9.343772
## 47.42857 11.95346 9.069875
## 47.57143 11.78007 8.804702
## 47.71429 11.61188 8.547468
## 47.85714 11.44843 8.297500
## 48.00000 11.28936 8.054214
## 48.14286 11.13432 7.817103
## 48.28571 10.98302 7.585718
##
## $四国地方$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 20.43440 22.45065
## 46.57143 21.66192 23.91768
## 46.71429 21.88144 24.25341
## 46.85714 22.09069 24.57343
## 47.00000 22.29100 24.87978
## 47.14286 22.48342 25.17406
## 47.28571 22.66882 25.45760
## 47.42857 22.84791 25.73150
## 47.57143 23.02130 25.99667
## 47.71429 23.18950 26.25391
## 47.85714 23.35294 26.50388
## 48.00000 23.51202 26.74716
## 48.14286 23.66706 26.98427
## 48.28571 23.81835 27.21566
##
##
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 116.67763 94.51471 84.51535 98.54193 117.91475 144.49396 141.51477
## [8] 132.56704 117.78222 107.94865 114.60824 126.82395 147.27796 143.40921
##
## $九州地方$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 94.21448 82.32321
## 46.57143 66.99762 52.43095
## 46.71429 53.82578 37.57970
## 46.85714 67.06833 50.40721
## 47.00000 84.85188 67.34945
## 47.14286 107.73347 88.27364
## 47.28571 101.73103 80.67079
## 47.42857 86.99808 62.87534
## 47.57143 67.48668 40.86183
## 47.71429 53.45458 24.60716
## 47.85714 57.51484 27.29142
## 48.00000 66.65663 34.80598
## 48.14286 83.51647 49.76318
## 48.28571 76.44487 40.99610
##
## $九州地方$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 139.1408 151.0320
## 46.57143 122.0318 136.5985
## 46.71429 115.2049 131.4510
## 46.85714 130.0155 146.6766
## 47.00000 150.9776 168.4801
## 47.14286 181.2544 200.7143
## 47.28571 181.2985 202.3587
## 47.42857 178.1360 202.2587
## 47.57143 168.0778 194.7026
## 47.71429 162.4427 191.2901
## 47.85714 171.7016 201.9251
## 48.00000 186.9913 218.8419
## 48.14286 211.0395 244.7927
## 48.28571 210.3735 245.8223
都道府県別
x_by_pref %>%
dplyr::select(pref, n) %>%
split(.$pref) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map(., forecast::auto.arima) %>%
purrr::map(., forecast::forecast) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道
## $北海道$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 261.6899 250.2059 249.4073 238.1762 230.4874 253.6377 256.1481 268.7122
## [9] 267.4404 270.4514 269.1841 273.7080 276.4356 282.1470
##
## $北海道$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 248.9786 242.2496
## 46.57143 235.3801 227.5317
## 46.71429 230.7230 220.8321
## 46.85714 218.4233 207.9668
## 47.00000 209.3558 198.1694
## 47.14286 231.7314 220.1349
## 47.28571 232.8265 220.4808
## 47.42857 242.1157 228.0363
## 47.57143 238.1574 222.6559
## 47.71429 238.3323 221.3295
## 47.85714 234.9911 216.8905
## 48.00000 237.5613 218.4264
## 48.14286 238.5081 218.4305
## 48.28571 242.2534 221.1350
##
## $北海道$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 274.4013 281.1303
## 46.57143 265.0318 272.8801
## 46.71429 268.0915 277.9824
## 46.85714 257.9290 268.3856
## 47.00000 251.6190 262.8054
## 47.14286 275.5441 287.1406
## 47.28571 279.4698 291.8155
## 47.42857 295.3088 309.3881
## 47.57143 296.7233 312.2248
## 47.71429 302.5705 319.5733
## 47.85714 303.3770 321.4777
## 48.00000 309.8547 328.9896
## 48.14286 314.3630 334.4406
## 48.28571 322.0406 343.1590
##
##
## $青森県
## $青森県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 2.279686 1.220966 1.845815 1.477033 1.694686 1.566229 1.642044 1.597298
## [9] 1.623707 1.608121 1.617319 1.611890 1.615095 1.613203
##
## $青森県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 -0.2422182 -1.577234
## 46.57143 -1.7279513 -3.289014
## 46.71429 -1.1840589 -2.787978
## 46.85714 -1.7771240 -3.499771
## 47.00000 -1.6808691 -3.467780
## 47.14286 -1.9743457 -3.848613
## 47.28571 -2.0282832 -3.971237
## 47.42857 -2.2139800 -4.231549
## 47.57143 -2.3144837 -4.399236
## 47.71429 -2.4581398 -4.610688
## 47.85714 -2.5701536 -4.786868
## 48.00000 -2.6950706 -4.975038
## 48.14286 -2.8071571 -5.148156
## 48.28571 -2.9219668 -5.322741
##
## $青森県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 4.801591 6.136606
## 46.57143 4.169883 5.730946
## 46.71429 4.875690 6.479609
## 46.85714 4.731191 6.453838
## 47.00000 5.070241 6.857152
## 47.14286 5.106804 6.981071
## 47.28571 5.312370 7.255324
## 47.42857 5.408577 7.426145
## 47.57143 5.561897 7.646649
## 47.71429 5.674381 7.826929
## 47.85714 5.804792 8.021507
## 48.00000 5.918851 8.198819
## 48.14286 6.037346 8.378345
## 48.28571 6.148374 8.549148
##
##
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 12.006200 7.371571 12.097086 8.275091 10.513658 8.852675 7.883580
## [8] 7.700147 8.456211 9.340291 7.733425 8.621521 7.638261 8.994877
##
## $岩手県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 10.523073 9.737953
## 46.57143 5.793079 4.957476
## 46.71429 10.405817 9.510514
## 46.85714 6.576066 5.676656
## 47.00000 8.712623 7.759212
## 47.14286 7.014538 6.041488
## 47.28571 5.928339 4.893297
## 47.42857 5.634293 4.540695
## 47.57143 6.257971 5.094293
## 47.71429 7.019159 5.790426
## 47.85714 5.289022 3.995034
## 48.00000 6.049476 4.687918
## 48.14286 4.940455 3.512323
## 48.28571 6.172072 4.677769
##
## $岩手県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 13.489328 14.274448
## 46.57143 8.950063 9.785665
## 46.71429 13.788355 14.683659
## 46.85714 9.974116 10.873526
## 47.00000 12.314694 13.268105
## 47.14286 10.690812 11.663863
## 47.28571 9.838820 10.873862
## 47.42857 9.766002 10.859599
## 47.57143 10.654451 11.818129
## 47.71429 11.661423 12.890156
## 47.85714 10.177828 11.471816
## 48.00000 11.193565 12.555124
## 48.14286 10.336067 11.764200
## 48.28571 11.817682 13.311985
##
##
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 15.65923 15.00746 16.34872 14.62264 16.75927 15.61303 16.11228 15.29517
## [9] 16.07077 15.62751 15.78002 15.85869 15.61589 15.95679
##
## $宮城県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 10.540169 7.830300
## 46.57143 9.797131 7.038952
## 46.71429 10.934549 8.068461
## 46.85714 9.174873 6.291001
## 47.00000 11.064524 8.049910
## 47.14286 9.889951 6.860337
## 47.28571 10.175716 7.033087
## 47.42857 9.234902 6.026788
## 47.57143 9.835192 6.534277
## 47.71429 9.298487 5.948107
## 47.85714 9.332361 5.919176
## 48.00000 9.286320 5.807116
## 48.14286 8.953152 5.426110
## 48.28571 9.156918 5.557282
##
## $宮城県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 20.77830 23.48817
## 46.57143 20.21778 22.97596
## 46.71429 21.76289 24.62898
## 46.85714 20.07040 22.95427
## 47.00000 22.45401 25.46862
## 47.14286 21.33610 24.36572
## 47.28571 22.04885 25.19148
## 47.42857 21.35545 24.56356
## 47.57143 22.30635 25.60726
## 47.71429 21.95652 25.30690
## 47.85714 22.22768 25.64087
## 48.00000 22.43106 25.91027
## 48.14286 22.27863 25.80568
## 48.28571 22.75667 26.35630
##
##
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 1.461043 1.461043 1.461043 1.461043 1.461043 1.461043 1.461043 1.461043
## [9] 1.461043 1.461043 1.461043 1.461043 1.461043 1.461043
##
## $秋田県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 0.09310936 -0.6310312
## 46.57143 0.08735918 -0.6398253
## 46.71429 0.08163298 -0.6485828
## 46.85714 0.07593044 -0.6573041
## 47.00000 0.07025129 -0.6659896
## 47.14286 0.06459523 -0.6746398
## 47.28571 0.05896199 -0.6832551
## 47.42857 0.05335129 -0.6918359
## 47.57143 0.04776287 -0.7003827
## 47.71429 0.04219646 -0.7088958
## 47.85714 0.03665180 -0.7173756
## 48.00000 0.03112864 -0.7258225
## 48.14286 0.02562673 -0.7342370
## 48.28571 0.02014583 -0.7426193
##
## $秋田県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 2.828978 3.553118
## 46.57143 2.834728 3.561912
## 46.71429 2.840454 3.570670
## 46.85714 2.846156 3.579391
## 47.00000 2.851836 3.588076
## 47.14286 2.857492 3.596727
## 47.28571 2.863125 3.605342
## 47.42857 2.868736 3.613923
## 47.57143 2.874324 3.622470
## 47.71429 2.879890 3.630983
## 47.85714 2.885435 3.639462
## 48.00000 2.890958 3.647909
## 48.14286 2.896460 3.656324
## 48.28571 2.901941 3.664706
##
##
## $山形県
## $山形県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 1.6741336 1.4699280 1.7305340 1.7345906 1.5433919 1.4527856 0.9436195
## [8] 1.3215480 1.2419312 1.1598546 1.0769010 0.9944580 0.9137222 0.8357064
##
## $山形県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 0.61892717 0.060334628
## 46.57143 0.39410294 -0.175404448
## 46.71429 0.62815078 0.044584334
## 46.85714 0.60111619 0.001091097
## 47.00000 0.37591712 -0.242106676
## 47.14286 0.24999058 -0.386730628
## 47.28571 -0.29441328 -0.949788222
## 47.42857 0.07327666 -0.587518269
## 47.57143 -0.03321603 -0.708238188
## 47.71429 -0.13907739 -0.826690428
## 47.85714 -0.24265147 -0.941180345
## 48.00000 -0.34261185 -1.050413860
## 48.14286 -0.43792404 -1.153442354
## 48.28571 -0.52780945 -1.249611135
##
## $山形県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 2.729340 3.287933
## 46.57143 2.545753 3.115260
## 46.71429 2.832917 3.416484
## 46.85714 2.868065 3.468090
## 47.00000 2.710867 3.328890
## 47.14286 2.655581 3.292302
## 47.28571 2.181652 2.837027
## 47.42857 2.569819 3.230614
## 47.57143 2.517078 3.192101
## 47.71429 2.458787 3.146400
## 47.85714 2.396453 3.094982
## 48.00000 2.331528 3.039330
## 48.14286 2.265368 2.980887
## 48.28571 2.199222 2.921024
##
##
## $福島県
## $福島県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 3.832207 3.832207 3.832207 3.832207 3.832207 3.832207 3.832207 3.832207
## [9] 3.832207 3.832207 3.832207 3.832207 3.832207 3.832207
##
## $福島県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 1.1863070 -0.2143480
## 46.57143 1.1202108 -0.3154334
## 46.71429 1.0556875 -0.4141132
## 46.85714 0.9926301 -0.5105512
## 47.00000 0.9309428 -0.6048937
## 47.14286 0.8705401 -0.6972717
## 47.28571 0.8113450 -0.7878028
## 47.42857 0.7532877 -0.8765938
## 47.57143 0.6963051 -0.9637412
## 47.71429 0.6403396 -1.0493330
## 47.85714 0.5853386 -1.1334497
## 48.00000 0.5312540 -1.2161651
## 48.14286 0.4780413 -1.2975469
## 48.28571 0.4256597 -1.3776576
##
## $福島県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 6.478107 7.878762
## 46.57143 6.544204 7.979848
## 46.71429 6.608727 8.078527
## 46.85714 6.671784 8.174966
## 47.00000 6.733471 8.269308
## 47.14286 6.793874 8.361686
## 47.28571 6.853069 8.452217
## 47.42857 6.911127 8.541008
## 47.57143 6.968109 8.628155
## 47.71429 7.024075 8.713747
## 47.85714 7.079076 8.797864
## 48.00000 7.133160 8.880579
## 48.14286 7.186373 8.961961
## 48.28571 7.238755 9.042072
##
##
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 38.87251 37.50691 38.73090 38.43764 38.43764 38.43764 38.43764 38.43764
## [9] 38.43764 38.43764 38.43764 38.43764 38.43764 38.43764
##
## $茨城県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 33.56143 30.74992
## 46.57143 31.74833 28.69992
## 46.71429 32.95505 29.89750
## 46.85714 32.58851 29.49217
## 47.00000 32.08805 28.72678
## 47.14286 31.62425 28.01746
## 47.28571 31.19007 27.35344
## 47.42857 30.78047 26.72701
## 47.57143 30.39169 26.13243
## 47.71429 30.02086 25.56528
## 47.85714 29.66568 25.02209
## 48.00000 29.32434 24.50005
## 48.14286 28.99533 23.99687
## 48.28571 28.67741 23.51065
##
## $茨城県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 44.18359 46.99510
## 46.57143 43.26549 46.31390
## 46.71429 44.50674 47.56429
## 46.85714 44.28677 47.38311
## 47.00000 44.78723 48.14850
## 47.14286 45.25103 48.85782
## 47.28571 45.68521 49.52184
## 47.42857 46.09481 50.14827
## 47.57143 46.48359 50.74286
## 47.71429 46.85443 51.31000
## 47.85714 47.20960 51.85319
## 48.00000 47.55094 52.37523
## 48.14286 47.87995 52.87841
## 48.28571 48.19788 53.36463
##
##
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 7.737705 8.779335 6.629754 8.200421 8.249254 6.740198 7.622445 7.587600
## [9] 7.587600 7.587600 7.587600 7.587600 7.587600 7.587600
##
## $栃木県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 4.539338 2.846224
## 46.57143 5.516553 3.789341
## 46.71429 3.303805 1.543154
## 46.85714 4.812482 3.019016
## 47.00000 4.800439 2.974746
## 47.14286 3.231563 1.374204
## 47.28571 4.054993 2.166498
## 47.42857 3.827159 1.836502
## 47.57143 3.752141 1.721771
## 47.71429 3.678562 1.609242
## 47.85714 3.606342 1.498791
## 48.00000 3.535409 1.390309
## 48.14286 3.465697 1.283694
## 48.28571 3.397145 1.178852
##
## $栃木県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 10.936071 12.62918
## 46.57143 12.042117 13.76933
## 46.71429 9.955703 11.71635
## 46.85714 11.588360 13.38183
## 47.00000 11.698069 13.52376
## 47.14286 10.248833 12.10619
## 47.28571 11.189897 13.07839
## 47.42857 11.348041 13.33870
## 47.57143 11.423060 13.45343
## 47.71429 11.496639 13.56596
## 47.85714 11.568858 13.67641
## 48.00000 11.639791 13.78489
## 48.14286 11.709503 13.89151
## 48.28571 11.778056 13.99635
##
##
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 26.24711 21.95037 24.22969 23.37897 23.45703 23.67726 23.42026 23.60983
## [9] 23.50350 23.54704 23.53939 23.53193 23.54241 23.53414
##
## $群馬県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 21.48389 18.96239
## 46.57143 16.22201 13.18960
## 46.71429 18.14630 14.92594
## 46.85714 17.15475 13.85985
## 47.00000 16.75690 13.21006
## 47.14286 16.80519 13.16734
## 47.28571 16.23241 12.42740
## 47.42857 16.18992 12.26206
## 47.57143 15.84103 11.78476
## 47.71429 15.64464 11.46136
## 47.85714 15.41492 11.11408
## 48.00000 15.18197 10.76177
## 48.14286 14.97876 10.44544
## 48.28571 14.75921 10.11404
##
## $群馬県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 31.01034 33.53184
## 46.57143 27.67873 30.71114
## 46.71429 30.31309 33.53345
## 46.85714 29.60319 32.89809
## 47.00000 30.15717 33.70400
## 47.14286 30.54932 34.18717
## 47.28571 30.60810 34.41311
## 47.42857 31.02975 34.95761
## 47.57143 31.16598 35.22225
## 47.71429 31.44945 35.63273
## 47.85714 31.66386 35.96469
## 48.00000 31.88188 36.30208
## 48.14286 32.10606 36.63938
## 48.28571 32.30907 36.95424
##
##
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 128.89917 96.31515 83.28949 73.96994 104.41525 136.19151 117.39013
## [8] 115.68871 92.87534 82.41998 83.30188 103.96207 124.91058 119.67675
##
## $埼玉県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 113.38056 105.16551
## 46.57143 80.23923 71.72915
## 46.71429 66.92680 58.26491
## 46.85714 57.30836 48.48825
## 47.00000 87.19652 78.08147
## 47.14286 118.03643 108.42571
## 47.28571 98.15033 87.96540
## 47.42857 93.53759 81.81149
## 47.57143 69.95300 57.81865
## 47.71429 59.07125 46.71117
## 47.85714 59.54721 46.97224
## 48.00000 79.54061 66.61267
## 48.14286 99.40832 85.90824
## 48.28571 92.88715 78.70559
##
## $埼玉県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 144.41777 152.63282
## 46.57143 112.39108 120.90116
## 46.71429 99.65217 108.31406
## 46.85714 90.63152 99.45163
## 47.00000 121.63398 130.74902
## 47.14286 154.34659 163.95731
## 47.28571 136.62993 146.81486
## 47.42857 137.83983 149.56592
## 47.57143 115.79768 127.93204
## 47.71429 105.76872 118.12880
## 47.85714 107.05656 119.63153
## 48.00000 128.38354 141.31148
## 48.14286 150.41284 163.91292
## 48.28571 146.46636 160.64791
##
##
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 99.14831 85.70471 82.56367 77.48185 81.48335 87.85083 92.42353 91.06943
## [9] 86.19170 81.58674 78.15560 83.74964 83.89662 89.18712
##
## $千葉県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 86.67183 80.06718
## 46.57143 71.72743 64.32831
## 46.71429 67.91338 60.15798
## 46.85714 62.34734 54.33561
## 47.00000 65.92320 57.68616
## 47.14286 71.88911 63.43949
## 47.28571 76.07391 67.41894
## 47.42857 73.52193 64.23284
## 47.57143 67.95500 58.30107
## 47.71429 62.79563 52.84822
## 47.85714 58.85710 48.64109
## 48.00000 63.96597 53.49314
## 48.14286 63.64219 52.92014
## 48.28571 68.47344 57.50828
##
## $千葉県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 111.62479 118.2294
## 46.57143 99.68199 107.0811
## 46.71429 97.21395 104.9693
## 46.85714 92.61635 100.6281
## 47.00000 97.04349 105.2805
## 47.14286 103.81254 112.2622
## 47.28571 108.77314 117.4281
## 47.42857 108.61694 117.9060
## 47.57143 104.42840 114.0823
## 47.71429 100.37786 110.3253
## 47.85714 97.45410 107.6701
## 48.00000 103.53330 114.0061
## 48.14286 104.15106 114.8731
## 48.28571 109.90079 120.8660
##
##
## $東京都
## $東京都$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 600.1379 455.7160 377.4551 362.8826 448.6601 520.9860 533.9859 550.9868
## [9] 426.9487 356.9806 386.2321 447.0546 514.9582 510.0052
##
## $東京都$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 550.9472 524.9073
## 46.57143 399.8599 370.2915
## 46.71429 317.4753 285.7239
## 46.85714 300.5081 267.4890
## 47.00000 385.9382 352.7353
## 47.14286 457.5717 424.0022
## 47.28571 469.5080 435.3755
## 47.42857 479.1128 441.0650
## 47.57143 350.8830 310.6162
## 47.71429 277.0945 234.8054
## 47.85714 302.9443 258.8544
## 48.00000 361.3293 315.9490
## 48.14286 426.5300 379.7190
## 48.28571 418.7065 370.3758
##
## $東京都$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 649.3285 675.3685
## 46.57143 511.5721 541.1405
## 46.71429 437.4349 469.1863
## 46.85714 425.2572 458.2763
## 47.00000 511.3819 544.5849
## 47.14286 584.4003 617.9697
## 47.28571 598.4638 632.5964
## 47.42857 622.8608 660.9086
## 47.57143 503.0144 543.2811
## 47.71429 436.8666 479.1557
## 47.85714 469.5198 513.6097
## 48.00000 532.7799 578.1602
## 48.14286 603.3863 650.1973
## 48.28571 601.3039 649.6345
##
##
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 195.38798 155.43425 106.92252 98.72461 158.98200 209.68612 204.32896
## [8] 186.33797 160.07899 133.36324 129.10292 154.85554 183.27609 184.30251
##
## $神奈川県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 173.30826 161.61996
## 46.57143 129.79577 116.22358
## 46.71429 80.46349 66.45693
## 46.85714 72.25389 58.24114
## 47.00000 132.45675 118.41512
## 47.14286 183.11756 169.05301
## 47.28571 176.85479 162.31084
## 47.42857 154.02282 136.91622
## 47.57143 124.87629 106.24111
## 47.71429 97.07297 77.86206
## 47.85714 92.48574 73.10178
## 48.00000 118.01751 98.51663
## 48.14286 146.11377 126.44122
## 48.28571 146.20618 126.03920
##
## $神奈川県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 217.4677 229.1560
## 46.57143 181.0727 194.6449
## 46.71429 133.3815 147.3881
## 46.85714 125.1953 139.2081
## 47.00000 185.5073 199.5489
## 47.14286 236.2547 250.3192
## 47.28571 231.8031 246.3471
## 47.42857 218.6531 235.7597
## 47.57143 195.2817 213.9169
## 47.71429 169.6535 188.8644
## 47.85714 165.7201 185.1041
## 48.00000 191.6936 211.1945
## 48.14286 220.4384 240.1110
## 48.28571 222.3988 242.5658
##
##
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 8.866461 9.763142 6.578379 4.186282 2.090872 5.824283 7.285758 6.349962
## [9] 5.341930 4.251101 4.561492 5.937628 6.749550 6.268674
##
## $新潟県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 5.9140162 4.35108658
## 46.57143 6.7327591 5.12857133
## 46.71429 3.5447334 1.93881845
## 46.85714 1.0991953 -0.53500982
## 47.00000 -1.0071798 -2.64718925
## 47.14286 2.6641354 0.99125429
## 47.28571 3.9214787 2.14053707
## 47.42857 2.9506438 1.15115369
## 47.57143 1.8601036 0.01693601
## 47.71429 0.7270966 -1.13839849
## 47.85714 1.0117610 -0.86735313
## 48.00000 2.3005364 0.37517668
## 48.14286 2.9988491 1.01334791
## 48.28571 2.4499911 0.42850259
##
## $新潟県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 11.818905 13.381835
## 46.57143 12.793525 14.397712
## 46.71429 9.612024 11.217939
## 46.85714 7.273369 8.907574
## 47.00000 5.188923 6.828933
## 47.14286 8.984431 10.657312
## 47.28571 10.650036 12.430978
## 47.42857 9.749279 11.548769
## 47.57143 8.823756 10.666924
## 47.71429 7.775105 9.640600
## 47.85714 8.111223 9.990337
## 48.00000 9.574719 11.500078
## 48.14286 10.500251 12.485752
## 48.28571 10.087357 12.108845
##
##
## $富山県
## $富山県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 2.907828 2.835532 2.766827 2.701535 2.639485 2.580518 2.524479 2.471223
## [9] 2.420613 2.372516 2.326808 2.283370 2.242090 2.202860
##
## $富山県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 0.46500834 -0.8281422
## 46.57143 0.23774933 -1.1374339
## 46.71429 0.03664048 -1.4086332
## 46.85714 -0.14293867 -1.6487121
## 47.00000 -0.30439370 -1.8627892
## 47.14286 -0.45033712 -2.0547748
## 47.28571 -0.58283511 -2.2277478
## 47.42857 -0.70356128 -2.3841907
## 47.57143 -0.81389737 -2.5261436
## 47.71429 -0.91500191 -2.6553087
## 47.85714 -1.00785880 -2.7731247
## 48.00000 -1.09331255 -2.8808203
## 48.14286 -1.17209453 -2.9794545
## 48.28571 -1.24484297 -3.0699465
##
## $富山県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 5.350647 6.643798
## 46.57143 5.433315 6.808498
## 46.71429 5.497014 6.942288
## 46.85714 5.546009 7.051782
## 47.00000 5.583365 7.141760
## 47.14286 5.611373 7.215810
## 47.28571 5.631793 7.276705
## 47.42857 5.646008 7.326637
## 47.57143 5.655123 7.367369
## 47.71429 5.660034 7.400340
## 47.85714 5.661475 7.426740
## 48.00000 5.660053 7.447560
## 48.14286 5.656274 7.463634
## 48.28571 5.650562 7.475666
##
##
## $石川県
## $石川県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 2.700633 2.700633 2.700633 2.700633 2.700633 2.700633 2.700633 2.700633
## [9] 2.700633 2.700633 2.700633 2.700633 2.700633 2.700633
##
## $石川県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 -0.9231718 -2.841498
## 46.57143 -1.1275240 -3.154028
## 46.71429 -1.3215071 -3.450700
## 46.85714 -1.5065557 -3.733707
## 47.00000 -1.6838010 -4.004780
## 47.14286 -1.8541542 -4.265313
## 47.28571 -2.0183618 -4.516447
## 47.42857 -2.1770444 -4.759131
## 47.57143 -2.3307249 -4.994165
## 47.71429 -2.4798484 -5.222230
## 47.85714 -2.6247977 -5.443911
## 48.00000 -2.7659050 -5.659716
## 48.14286 -2.9034604 -5.870088
## 48.28571 -3.0377194 -6.075420
##
## $石川県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 6.324439 8.242765
## 46.57143 6.528791 8.555295
## 46.71429 6.722774 8.851967
## 46.85714 6.907822 9.134974
## 47.00000 7.085068 9.406047
## 47.14286 7.255421 9.666580
## 47.28571 7.419629 9.917714
## 47.42857 7.578311 10.160398
## 47.57143 7.731992 10.395432
## 47.71429 7.881115 10.623497
## 47.85714 8.026065 10.845178
## 48.00000 8.167172 11.060982
## 48.14286 8.304727 11.271355
## 48.28571 8.438986 11.476687
##
##
## $福井県
## $福井県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 2.834694 2.714897 2.454612 2.233980 2.046958 1.888428 1.754049 1.640141
## [9] 1.543586 1.461740 1.392363 1.333554 1.283705 1.241450
##
## $福井県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 0.82334509 -0.2413986
## 46.57143 0.56366598 -0.5751271
## 46.71429 0.07782427 -1.1803713
## 46.85714 -0.29247441 -1.6298984
## 47.00000 -0.58177870 -1.9733480
## 47.14286 -0.81141047 -2.2406187
## 47.28571 -0.99574341 -2.4513954
## 47.42857 -1.14499105 -2.6193507
## 47.57143 -1.26666422 -2.7543206
## 47.71429 -1.36642022 -2.8635577
## 47.85714 -1.44859673 -2.9525098
## 48.00000 -1.51656607 -3.0253287
## 48.14286 -1.57297973 -3.0852173
## 48.28571 -1.61994228 -3.1346718
##
## $福井県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 4.846042 5.910786
## 46.57143 4.866128 6.004922
## 46.71429 4.831400 6.089596
## 46.85714 4.760434 6.097858
## 47.00000 4.675695 6.067265
## 47.14286 4.588267 6.017475
## 47.28571 4.503841 5.959493
## 47.42857 4.425272 5.899632
## 47.57143 4.353835 5.841492
## 47.71429 4.289900 5.787037
## 47.85714 4.233322 5.737235
## 48.00000 4.183675 5.692437
## 48.14286 4.140390 5.652627
## 48.28571 4.102842 5.617572
##
##
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 2.428706 3.651437 3.544665 3.619113 3.941709 4.282857 4.184319 4.020785
## [9] 3.966022 3.947683 3.941542 3.939485 3.938797 3.938566
##
## $山梨県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 0.5377027 -0.46333392
## 46.57143 1.5464405 0.43212272
## 46.71429 1.3555140 0.19664754
## 46.85714 1.3751846 0.18732042
## 47.00000 1.6518173 0.43962178
## 47.14286 1.9502518 0.71544530
## 47.28571 1.8105272 0.55391777
## 47.42857 1.6326390 0.36843068
## 47.57143 1.5521211 0.27427910
## 47.71429 1.5037608 0.21002649
## 47.85714 1.4663803 0.15610888
## 48.00000 1.4329416 0.10605748
## 48.14286 1.4010818 0.05769670
## 48.28571 1.3699996 0.01028272
##
## $山梨県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 4.319709 5.320746
## 46.57143 5.756433 6.870751
## 46.71429 5.733816 6.892682
## 46.85714 5.863042 7.050906
## 47.00000 6.231601 7.443796
## 47.14286 6.615462 7.850268
## 47.28571 6.558111 7.814720
## 47.42857 6.408932 7.673140
## 47.57143 6.379923 7.657765
## 47.71429 6.391605 7.685340
## 47.85714 6.416703 7.726975
## 48.00000 6.446029 7.772913
## 48.14286 6.476511 7.819897
## 48.28571 6.507132 7.866849
##
##
## $長野県
## $長野県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 14.61777 14.61777 14.61777 14.61777 14.61777 14.61777 14.61777 14.61777
## [9] 14.61777 14.61777 14.61777 14.61777 14.61777 14.61777
##
## $長野県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 11.223307 9.426386
## 46.57143 10.959528 9.022972
## 46.71429 10.713531 8.646751
## 46.85714 10.482140 8.292870
## 47.00000 10.263027 7.957765
## 47.14286 10.054423 7.638732
## 47.28571 9.854946 7.333660
## 47.42857 9.663495 7.040860
## 47.57143 9.479171 6.758962
## 47.71429 9.301235 6.486831
## 47.85714 9.129064 6.223518
## 48.00000 8.962131 5.968217
## 48.14286 8.799987 5.720239
## 48.28571 8.642240 5.478986
##
## $長野県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 18.01223 19.80915
## 46.57143 18.27601 20.21257
## 46.71429 18.52201 20.58879
## 46.85714 18.75340 20.94267
## 47.00000 18.97251 21.27777
## 47.14286 19.18112 21.59681
## 47.28571 19.38059 21.90188
## 47.42857 19.57204 22.19468
## 47.57143 19.75637 22.47658
## 47.71429 19.93430 22.74871
## 47.85714 20.10648 23.01202
## 48.00000 20.27341 23.26732
## 48.14286 20.43555 23.51530
## 48.28571 20.59330 23.75655
##
##
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 24.13770 19.70403 21.31151 19.50297 25.67680 21.68669 25.65522 23.63851
## [9] 23.63851 23.63851 23.63851 23.63851 23.63851 23.63851
##
## $岐阜県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 20.22924 18.16023
## 46.57143 15.49419 13.26563
## 46.71429 16.82047 14.44306
## 46.85714 14.74733 12.22985
## 47.00000 20.67053 18.02037
## 47.14286 16.44175 13.66524
## 47.28571 20.18200 17.28465
## 47.42857 17.53106 14.29798
## 47.57143 17.20480 13.79900
## 47.71429 16.89430 13.32414
## 47.85714 16.59749 12.87020
## 48.00000 16.31269 12.43463
## 48.14286 16.03855 12.01538
## 48.28571 15.77396 11.61073
##
## $岐阜県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 28.04616 30.11518
## 46.57143 23.91387 26.14242
## 46.71429 25.80255 28.17996
## 46.85714 24.25861 26.77609
## 47.00000 30.68308 33.33324
## 47.14286 26.93164 29.70815
## 47.28571 31.12844 34.02579
## 47.42857 29.74595 32.97903
## 47.57143 30.07221 33.47801
## 47.71429 30.38271 33.95287
## 47.85714 30.67952 34.40681
## 48.00000 30.96433 34.84238
## 48.14286 31.23846 35.26164
## 48.28571 31.50305 35.66628
##
##
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 55.62959 52.43770 54.24945 48.18553 50.34311 57.48946 53.49613 53.12759
## [9] 53.40479 53.40479 53.40479 53.40479 53.40479 53.40479
##
## $静岡県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 48.73935 45.09188
## 46.57143 44.57499 40.41272
## 46.71429 45.99089 41.61907
## 46.85714 39.54925 34.97748
## 47.00000 41.34495 36.58161
## 47.14286 48.14342 43.19593
## 47.28571 43.81470 38.68967
## 47.42857 42.41589 36.74545
## 47.57143 42.08594 36.09411
## 47.71429 41.62013 35.38171
## 47.85714 41.17204 34.69642
## 48.00000 40.73980 34.03535
## 48.14286 40.32182 33.39612
## 48.28571 39.91680 32.77669
##
## $静岡県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 62.51983 66.16730
## 46.57143 60.30041 64.46268
## 46.71429 62.50800 66.87982
## 46.85714 56.82180 61.39357
## 47.00000 59.34126 64.10460
## 47.14286 66.83549 71.78299
## 47.28571 63.17755 68.30258
## 47.42857 63.83930 69.50973
## 47.57143 64.72363 70.71547
## 47.71429 65.18945 71.42787
## 47.85714 65.63754 72.11316
## 48.00000 66.06978 72.77422
## 48.14286 66.48775 73.41346
## 48.28571 66.89278 74.03289
##
##
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 223.1772 187.9509 155.9649 165.8491 206.5703 219.5002 241.4838 234.8764
## [9] 213.3310 193.7725 199.8163 224.7181 232.6249 246.0683
##
## $愛知県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 205.7495 196.5238
## 46.57143 167.1830 156.1891
## 46.71429 131.2204 118.1214
## 46.85714 138.0386 123.3167
## 47.00000 175.8879 159.6457
## 47.14286 186.2291 168.6165
## 47.28571 205.7994 186.9093
## 47.42857 192.1239 169.4922
## 47.57143 166.2164 141.2754
## 47.71429 142.1416 114.8098
## 47.85714 144.2076 114.7701
## 48.00000 165.3406 133.9081
## 48.14286 169.7230 136.4248
## 48.28571 179.8228 144.7546
##
## $愛知県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 240.6050 249.8307
## 46.57143 208.7187 219.7126
## 46.71429 180.7094 193.8083
## 46.85714 193.6595 208.3814
## 47.00000 237.2526 253.4948
## 47.14286 252.7712 270.3839
## 47.28571 277.1681 296.0582
## 47.42857 277.6288 300.2606
## 47.57143 260.4457 285.3867
## 47.71429 245.4034 272.7352
## 47.85714 255.4251 284.8626
## 48.00000 284.0956 315.5282
## 48.14286 295.5269 328.8251
## 48.28571 312.3137 347.3819
##
##
## $三重県
## $三重県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 18.54795 17.17929 17.01244 16.39063 18.95593 19.94725 18.64982 19.05881
## [9] 18.65369 18.15209 17.65752 18.64641 19.55955 18.38333
##
## $三重県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 14.52796 12.399901
## 46.57143 12.73151 10.377006
## 46.71429 12.38267 9.931822
## 46.85714 11.41236 8.777024
## 47.00000 13.79786 11.067345
## 47.14286 14.49301 11.605707
## 47.28571 13.01871 10.037781
## 47.42857 13.02687 9.833751
## 47.57143 12.40160 9.091938
## 47.71429 11.63687 8.187920
## 47.85714 10.93245 7.372415
## 48.00000 11.68121 7.994058
## 48.14286 12.39361 8.600194
## 48.28571 10.99531 7.084326
##
## $三重県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 22.56794 24.69599
## 46.57143 21.62706 23.98157
## 46.71429 21.64221 24.09307
## 46.85714 21.36890 24.00423
## 47.00000 24.11400 26.84452
## 47.14286 25.40149 28.28879
## 47.28571 24.28093 27.26185
## 47.42857 25.09075 28.28387
## 47.57143 24.90579 28.21544
## 47.71429 24.66730 28.11625
## 47.85714 24.38258 27.94262
## 48.00000 25.61161 29.29877
## 48.14286 26.72549 30.51891
## 48.28571 25.77136 29.68234
##
##
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 7.289527 9.982049 8.230259 9.033755 8.495314 8.734361 8.568493 8.639347
## [9] 8.588121 8.609026 8.593160 8.599293 8.594363 8.596149
##
## $滋賀県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 3.379052 1.3089712
## 46.57143 6.002716 3.8961836
## 46.71429 3.831442 1.5028497
## 46.85714 4.546898 2.1716993
## 47.00000 3.847564 1.3871943
## 47.14286 3.999883 1.4936015
## 47.28571 3.730514 1.1694432
## 47.42857 3.718784 1.1139951
## 47.57143 3.581582 0.9312810
## 47.71429 3.523477 0.8313500
## 47.85714 3.428568 0.6945989
## 48.00000 3.358527 0.5842339
## 48.14286 3.278198 0.4639901
## 48.28571 3.206158 0.3528688
##
## $滋賀県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 11.20000 13.27008
## 46.57143 13.96138 16.06791
## 46.71429 12.62908 14.95767
## 46.85714 13.52061 15.89581
## 47.00000 13.14306 15.60343
## 47.14286 13.46884 15.97512
## 47.28571 13.40647 15.96754
## 47.42857 13.55991 16.16470
## 47.57143 13.59466 16.24496
## 47.71429 13.69457 16.38670
## 47.85714 13.75775 16.49172
## 48.00000 13.84006 16.61435
## 48.14286 13.91053 16.72474
## 48.28571 13.98614 16.83943
##
##
## $京都府
## $京都府$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 27.84577 25.43823 23.26327 29.48264 29.93960 26.17749 27.31259 28.72929
## [9] 26.69638 25.31287 24.60370 27.75645 28.54847 27.79576
##
## $京都府$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 20.88661 17.20266
## 46.57143 17.85079 13.83425
## 46.71429 15.56789 11.49420
## 46.85714 21.72906 17.62457
## 47.00000 22.09695 17.94531
## 47.14286 18.19593 13.97074
## 47.28571 19.15618 14.83844
## 47.42857 20.21996 15.71539
## 47.57143 17.94097 13.30614
## 47.71429 16.35771 11.61714
## 47.85714 15.46294 10.62411
## 48.00000 18.43136 13.49496
## 48.14286 19.03771 14.00301
## 48.28571 18.09928 12.96627
##
## $京都府$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 34.80493 38.48889
## 46.57143 33.02566 37.04221
## 46.71429 30.95865 35.03234
## 46.85714 37.23621 41.34071
## 47.00000 37.78225 41.93390
## 47.14286 34.15905 38.38423
## 47.28571 35.46899 39.78674
## 47.42857 37.23863 41.74320
## 47.57143 35.45179 40.08662
## 47.71429 34.26803 39.00860
## 47.85714 33.74447 38.58329
## 48.00000 37.08153 42.01793
## 48.14286 38.05924 43.09394
## 48.28571 37.49225 42.62526
##
##
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 414.4818 442.8279 294.5703 310.1138 365.1788 385.2637 418.5108 444.5208
## [9] 481.4745 358.6552 336.8548 392.0778 404.0259 433.4252
##
## $大阪府$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 382.0295 364.8503
## 46.57143 405.7023 386.0493
## 46.71429 255.8356 235.3307
## 46.85714 269.8342 248.5114
## 47.00000 323.4114 301.3010
## 47.14286 342.0597 319.1888
## 47.28571 373.9164 350.3095
## 47.42857 392.8001 365.4208
## 47.57143 426.2116 396.9572
## 47.71429 301.0649 270.5784
## 47.85714 277.0276 245.3570
## 48.00000 330.0943 297.2823
## 48.14286 339.9587 306.0436
## 48.28571 367.3400 332.3566
##
## $大阪府$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 446.9341 464.1133
## 46.57143 479.9534 499.6065
## 46.71429 333.3050 353.8099
## 46.85714 350.3934 371.7162
## 47.00000 406.9463 429.0566
## 47.14286 428.4678 451.3387
## 47.28571 463.1053 486.7121
## 47.42857 496.2416 523.6209
## 47.57143 536.7374 565.9919
## 47.71429 416.2455 446.7320
## 47.85714 396.6821 428.3527
## 48.00000 454.0612 486.8733
## 48.14286 468.0931 502.0082
## 48.28571 499.5104 534.4938
##
##
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 144.47299 136.22986 99.72455 99.72455 113.85564 162.72565 115.03323
## [8] 139.45233 134.59881 113.10465 113.10465 121.42497 150.19941 122.11833
##
## $兵庫県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 131.84013 125.15270
## 46.57143 122.88311 115.81777
## 46.71429 85.70021 78.27617
## 46.85714 85.05388 77.28770
## 47.00000 98.56593 90.47206
## 47.14286 146.84102 138.43220
## 47.28571 98.57516 89.86279
## 47.42857 119.23336 108.53009
## 47.57143 113.25349 101.95396
## 47.71429 90.68950 78.82364
## 47.85714 89.66846 77.26208
## 48.00000 97.01039 84.08610
## 48.14286 124.84417 111.42192
## 48.28571 95.85611 81.95373
##
## $兵庫県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 157.1058 163.7933
## 46.57143 149.5766 156.6419
## 46.71429 113.7489 121.1729
## 46.85714 114.3952 122.1614
## 47.00000 129.1453 137.2392
## 47.14286 178.6103 187.0191
## 47.28571 131.4913 140.2037
## 47.42857 159.6713 170.3746
## 47.57143 155.9441 167.2437
## 47.71429 135.5198 147.3857
## 47.85714 136.5409 148.9472
## 48.00000 145.8396 158.7638
## 48.14286 175.5546 188.9769
## 48.28571 148.3806 162.2829
##
##
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 20.12058 19.11775 20.16571 19.47452 18.73567 19.99656 20.61794 19.92601
## [9] 19.92601 19.92601 19.92601 19.92601 19.92601 19.92601
##
## $奈良県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 15.74747 13.432494
## 46.57143 14.56862 12.160461
## 46.71429 15.44712 12.949250
## 46.85714 14.59235 12.007880
## 47.00000 13.69521 11.026957
## 47.14286 14.80265 12.053157
## 47.28571 15.27498 12.446585
## 47.42857 14.29487 11.313923
## 47.57143 14.12255 11.050388
## 47.71429 13.95521 10.794456
## 47.85714 13.79243 10.545504
## 48.00000 13.63385 10.302991
## 48.14286 13.47918 10.066440
## 48.28571 13.32813 9.835433
##
## $奈良県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 24.49368 26.80866
## 46.57143 23.66688 26.07504
## 46.71429 24.88430 27.38217
## 46.85714 24.35670 26.94117
## 47.00000 23.77612 26.44437
## 47.14286 25.19047 27.93996
## 47.28571 25.96090 28.78930
## 47.42857 25.55714 28.53809
## 47.57143 25.72946 28.80162
## 47.71429 25.89680 29.05755
## 47.85714 26.05958 29.30651
## 48.00000 26.21816 29.54902
## 48.14286 26.37283 29.78557
## 48.28571 26.52388 30.01658
##
##
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 9.578561 8.505071 8.815395 9.480171 9.221813 8.419769 8.358110 8.909241
## [9] 9.239068 8.650337 8.918860 9.141074 9.016168 8.308704
##
## $和歌山県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 7.444582 6.314923
## 46.57143 6.175717 4.942632
## 46.71429 6.429278 5.166143
## 46.85714 6.994092 5.678041
## 47.00000 6.617288 5.238535
## 47.14286 5.709368 4.274569
## 47.28571 5.549641 4.062928
## 47.42857 6.036851 4.516299
## 47.57143 6.286792 4.723952
## 47.71429 5.614275 4.007081
## 47.85714 5.803975 4.155054
## 48.00000 5.950394 4.261350
## 48.14286 5.751077 4.022642
## 48.28571 4.970669 3.203620
##
## $和歌山県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 11.71254 12.84220
## 46.57143 10.83442 12.06751
## 46.71429 11.20151 12.46465
## 46.85714 11.96625 13.28230
## 47.00000 11.82634 13.20509
## 47.14286 11.13017 12.56497
## 47.28571 11.16658 12.65329
## 47.42857 11.78163 13.30218
## 47.57143 12.19134 13.75418
## 47.71429 11.68640 13.29359
## 47.85714 12.03375 13.68267
## 48.00000 12.33175 14.02080
## 48.14286 12.28126 14.00969
## 48.28571 11.64674 13.41379
##
##
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 0.8072075 0.7023488 0.4502036 0.5090922 0.3450343 0.4217498 0.4613927
## [8] 0.4588347 0.4676223 0.4070991 0.3632656 0.2712461 0.2763247 0.4603020
##
## $鳥取県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 -0.1330553 -0.6308004
## 46.57143 -0.2503828 -0.7547284
## 46.71429 -0.5131305 -1.0230888
## 46.85714 -0.4711424 -0.9900473
## 47.00000 -0.6384659 -1.1590994
## 47.14286 -0.5641115 -1.0859949
## 47.28571 -0.5263308 -1.0492001
## 47.42857 -0.5288917 -1.0517625
## 47.57143 -0.5206559 -1.0438189
## 47.71429 -0.5816369 -1.1050421
## 47.85714 -0.6257910 -1.1493660
## 48.00000 -0.7182144 -1.2420032
## 48.14286 -0.7135260 -1.2375213
## 48.28571 -0.5299277 -1.0541236
##
## $鳥取県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 1.747470 2.245215
## 46.57143 1.655080 2.159426
## 46.71429 1.413538 1.923496
## 46.85714 1.489327 2.008232
## 47.00000 1.328534 1.849168
## 47.14286 1.407611 1.929494
## 47.28571 1.449116 1.971986
## 47.42857 1.446561 1.969432
## 47.57143 1.455901 1.979063
## 47.71429 1.395835 1.919240
## 47.85714 1.352322 1.875897
## 48.00000 1.260707 1.784496
## 48.14286 1.266175 1.790171
## 48.28571 1.450532 1.974728
##
##
## $島根県
## $島根県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 0.4559748 0.4559748 0.4559748 0.4559748 0.4559748 0.4559748 0.4559748
## [8] 0.4559748 0.4559748 0.4559748 0.4559748 0.4559748 0.4559748 0.4559748
##
## $島根県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 -6.186758 -9.703209
## 46.57143 -6.186758 -9.703209
## 46.71429 -6.186758 -9.703209
## 46.85714 -6.186758 -9.703209
## 47.00000 -6.186758 -9.703209
## 47.14286 -6.186758 -9.703209
## 47.28571 -6.186758 -9.703209
## 47.42857 -6.186758 -9.703209
## 47.57143 -6.186758 -9.703209
## 47.71429 -6.186758 -9.703209
## 47.85714 -6.186758 -9.703209
## 48.00000 -6.186758 -9.703209
## 48.14286 -6.186758 -9.703209
## 48.28571 -6.186758 -9.703209
##
## $島根県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 7.098708 10.61516
## 46.57143 7.098708 10.61516
## 46.71429 7.098708 10.61516
## 46.85714 7.098708 10.61516
## 47.00000 7.098708 10.61516
## 47.14286 7.098708 10.61516
## 47.28571 7.098708 10.61516
## 47.42857 7.098708 10.61516
## 47.57143 7.098708 10.61516
## 47.71429 7.098708 10.61516
## 47.85714 7.098708 10.61516
## 48.00000 7.098708 10.61516
## 48.14286 7.098708 10.61516
## 48.28571 7.098708 10.61516
##
##
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 19.96307 15.78092 15.34203 14.57816 15.49336 15.59932 17.50204 16.31192
## [9] 16.04390 15.98354 15.96995 15.96689 15.96620 15.96605
##
## $岡山県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 16.67750 14.938221
## 46.57143 12.24525 10.373574
## 46.71429 11.70318 9.776893
## 46.85714 10.86079 8.892940
## 47.00000 11.70357 9.697375
## 47.14286 11.73943 9.696125
## 47.28571 13.57351 11.493880
## 47.42857 12.16648 9.972025
## 47.57143 11.78732 9.534026
## 47.71429 11.63574 9.334153
## 47.85714 11.53638 9.189388
## 48.00000 11.44996 9.058840
## 48.14286 11.36759 8.933238
## 48.28571 11.28723 8.810408
##
## $岡山県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 23.24864 24.98791
## 46.57143 19.31659 21.18826
## 46.71429 18.98088 20.90717
## 46.85714 18.29552 20.26337
## 47.00000 19.28315 21.28934
## 47.14286 19.45920 21.50251
## 47.28571 21.43056 23.51020
## 47.42857 20.45735 22.65181
## 47.57143 20.30048 22.55378
## 47.71429 20.33135 22.63294
## 47.85714 20.40352 22.75052
## 48.00000 20.48382 22.87494
## 48.14286 20.56481 22.99917
## 48.28571 20.64487 23.12168
##
##
## $広島県
## $広島県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 7.234651 9.995929 11.868805 9.781222 11.045840 9.754933 10.643739
## [8] 10.534486 10.414751 11.040192 10.554818 9.602897 11.220927 11.053411
##
## $広島県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 3.609573 1.6905730
## 46.57143 6.036321 3.9402306
## 46.71429 7.739537 5.5536341
## 46.85714 5.004138 2.4753028
## 47.00000 5.697272 2.8659115
## 47.14286 4.140126 1.1678272
## 47.28571 4.735263 1.6075041
## 47.42857 4.345823 1.0697419
## 47.57143 3.940229 0.5128238
## 47.71429 4.329687 0.7773608
## 47.85714 3.618471 -0.0534091
## 48.00000 2.427766 -1.3705187
## 48.14286 3.817318 -0.1019163
## 48.28571 3.438413 -0.5927227
##
## $広島県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 10.85973 12.77873
## 46.57143 13.95554 16.05163
## 46.71429 15.99807 18.18398
## 46.85714 14.55831 17.08714
## 47.00000 16.39441 19.22577
## 47.14286 15.36974 18.34204
## 47.28571 16.55222 19.67997
## 47.42857 16.72315 19.99923
## 47.57143 16.88927 20.31668
## 47.71429 17.75070 21.30302
## 47.85714 17.49116 21.16304
## 48.00000 16.77803 20.57631
## 48.14286 18.62454 22.54377
## 48.28571 18.66841 22.69954
##
##
## $山口県
## $山口県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 3.123485 3.818295 1.287554 2.354420 2.019833 2.835451 1.232873 2.884820
## [9] 1.679278 3.030333 1.853489 3.117673 1.977717 3.166906
##
## $山口県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 0.5039618 -0.8827301
## 46.57143 0.9608014 -0.5518644
## 46.71429 -1.7950243 -3.4268425
## 46.85714 -0.8232394 -2.5053905
## 47.00000 -1.2684015 -3.0090876
## 47.14286 -0.4948843 -2.2578572
## 47.28571 -2.1580654 -3.9531198
## 47.42857 -0.6235540 -2.4807748
## 47.57143 -1.9045209 -3.8016696
## 47.71429 -0.5879460 -2.5033473
## 47.85714 -1.8083156 -3.7467578
## 48.00000 -0.5625051 -2.5106736
## 48.14286 -1.7299105 -3.6926099
## 48.28571 -0.5513881 -2.5197342
##
## $山口県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 5.743009 7.129701
## 46.57143 6.675789 8.188455
## 46.71429 4.370132 6.001950
## 46.85714 5.532079 7.214230
## 47.00000 5.308067 7.048753
## 47.14286 6.165786 7.928759
## 47.28571 4.623811 6.418866
## 47.42857 6.393193 8.250414
## 47.57143 5.263077 7.160226
## 47.71429 6.648612 8.564014
## 47.85714 5.515293 7.453736
## 48.00000 6.797851 8.746019
## 48.14286 5.685344 7.648044
## 48.28571 6.885200 8.853546
##
##
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 0.7210946 0.7936005 0.6533883 0.3345022 0.4069932 0.6040399 0.4959713
## [8] 0.4773350 0.5730845 0.6922471 0.7277446 0.7466194 0.6033288 0.6286992
##
## $徳島県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 -0.9826874 -1.884615
## 46.57143 -0.9931227 -1.938957
## 46.71429 -1.1515818 -2.107075
## 46.85714 -1.4885322 -2.453588
## 47.00000 -1.4339283 -2.408453
## 47.14286 -1.2545966 -2.238499
## 47.28571 -1.3802128 -2.373405
## 47.42857 -1.4904520 -2.532136
## 47.57143 -1.4278227 -2.487039
## 47.71429 -1.3320289 -2.403616
## 47.85714 -1.3196336 -2.403450
## 48.00000 -1.3236030 -2.419513
## 48.14286 -1.4894886 -2.597359
## 48.28571 -1.4864718 -2.606176
##
## $徳島県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 2.424877 3.326804
## 46.57143 2.580324 3.526158
## 46.71429 2.458358 3.413852
## 46.85714 2.157537 3.122593
## 47.00000 2.247915 3.222440
## 47.14286 2.462676 3.446579
## 47.28571 2.372155 3.365347
## 47.42857 2.445122 3.486805
## 47.57143 2.573992 3.633208
## 47.71429 2.716523 3.788110
## 47.85714 2.775123 3.858939
## 48.00000 2.816842 3.912751
## 48.14286 2.696146 3.804017
## 48.28571 2.743870 3.863574
##
##
## $香川県
## $香川県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 0.9544989 1.1718759 1.1718759 1.1718759 1.1718759 1.1718759 1.1718759
## [8] 1.1718759 1.1718759 1.1718759 1.1718759 1.1718759 1.1718759 1.1718759
##
## $香川県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 -0.4835802 -1.244853
## 46.57143 -0.3220012 -1.112812
## 46.71429 -0.3288181 -1.123238
## 46.85714 -0.3356042 -1.133616
## 47.00000 -0.3423599 -1.143948
## 47.14286 -0.3490856 -1.154234
## 47.28571 -0.3557817 -1.164475
## 47.42857 -0.3624485 -1.174671
## 47.57143 -0.3690865 -1.184823
## 47.71429 -0.3756960 -1.194931
## 47.85714 -0.3822775 -1.204997
## 48.00000 -0.3888311 -1.215020
## 48.14286 -0.3953574 -1.225001
## 48.28571 -0.4018566 -1.234940
##
## $香川県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 2.392578 3.153851
## 46.57143 2.665753 3.456564
## 46.71429 2.672570 3.466989
## 46.85714 2.679356 3.477368
## 47.00000 2.686112 3.487700
## 47.14286 2.692837 3.497986
## 47.28571 2.699534 3.508227
## 47.42857 2.706200 3.518423
## 47.57143 2.712838 3.528575
## 47.71429 2.719448 3.538683
## 47.85714 2.726029 3.548748
## 48.00000 2.732583 3.558771
## 48.14286 2.739109 3.568752
## 48.28571 2.745608 3.578692
##
##
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 14.82164 14.63899 15.28397 14.75724 15.18740 14.83611 15.12299 14.88871
## [9] 15.08003 14.92379 15.05139 14.94718 15.03228 14.96278
##
## $愛媛県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 12.57603 11.387270
## 46.57143 12.04334 10.669292
## 46.71429 12.34678 10.791932
## 46.85714 11.62302 9.963865
## 47.00000 11.78533 9.984378
## 47.14286 11.24945 9.350783
## 47.28571 11.31180 9.294278
## 47.42857 10.90377 8.794270
## 47.57143 10.89886 8.685477
## 47.71429 10.57859 8.278388
## 47.85714 10.52980 8.136216
## 48.00000 10.27034 7.794564
## 48.14286 10.19373 7.632360
## 48.28571 9.97684 7.337441
##
## $愛媛県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 17.06725 18.25601
## 46.57143 17.23463 18.60868
## 46.71429 18.22116 19.77601
## 46.85714 17.89146 19.55062
## 47.00000 18.58947 20.39041
## 47.14286 18.42277 20.32144
## 47.28571 18.93418 20.95170
## 47.42857 18.87365 20.98315
## 47.57143 19.26121 21.47459
## 47.71429 19.26898 21.56919
## 47.85714 19.57297 21.96656
## 48.00000 19.62403 22.09980
## 48.14286 19.87083 22.43220
## 48.28571 19.94873 22.58813
##
##
## $高知県
## $高知県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 0.4125725 0.4157684 0.4187185 0.4214417 0.4239554 0.4262758 0.4284177
## [8] 0.4303949 0.4322200 0.4339047 0.4354598 0.4368953 0.4382204 0.4394436
##
## $高知県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 -1.261524 -2.147738
## 46.57143 -1.279471 -2.176877
## 46.71429 -1.294331 -2.201164
## 46.85714 -1.306638 -2.221428
## 47.00000 -1.316829 -2.238345
## 47.14286 -1.325261 -2.252469
## 47.28571 -1.332230 -2.264261
## 47.42857 -1.337979 -2.274099
## 47.57143 -1.342710 -2.282302
## 47.71429 -1.346593 -2.289132
## 47.85714 -1.349769 -2.294812
## 48.00000 -1.352354 -2.299526
## 48.14286 -1.354448 -2.303430
## 48.28571 -1.356133 -2.306654
##
## $高知県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 2.086669 2.972883
## 46.57143 2.111008 3.008414
## 46.71429 2.131768 3.038601
## 46.85714 2.149521 3.064311
## 47.00000 2.164740 3.086255
## 47.14286 2.177813 3.105021
## 47.28571 2.189066 3.121096
## 47.42857 2.198769 3.134889
## 47.57143 2.207150 3.146742
## 47.71429 2.214403 3.156942
## 47.85714 2.220688 3.165731
## 48.00000 2.226145 3.173316
## 48.14286 2.230889 3.179870
## 48.28571 2.235020 3.185541
##
##
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 49.52272 45.49001 37.41783 38.95650 45.70089 53.40355 56.77989 53.61039
## [9] 51.72440 46.43263 47.60251 52.31411 57.66729 60.01259
##
## $福岡県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 35.62009 28.260480
## 46.57143 28.97399 20.230941
## 46.71429 19.38460 9.838391
## 46.85714 19.72693 9.547411
## 47.00000 25.40647 14.663261
## 47.14286 32.11673 20.848171
## 47.28571 34.55109 22.783877
## 47.42857 28.86713 15.768844
## 47.57143 25.30475 11.319025
## 47.71429 18.61074 3.882726
## 47.85714 18.49952 3.093335
## 48.00000 22.00121 5.954528
## 48.14286 26.19646 9.536813
## 48.28571 27.42674 10.176835
##
## $福岡県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 63.42534 70.78495
## 46.57143 62.00602 70.74908
## 46.71429 55.45105 64.99726
## 46.85714 58.18607 68.36559
## 47.00000 65.99530 76.73851
## 47.14286 74.69036 85.95892
## 47.28571 79.00869 90.77591
## 47.42857 78.35365 91.45194
## 47.57143 78.14406 92.12978
## 47.71429 74.25451 88.98253
## 47.85714 76.70549 92.11168
## 48.00000 82.62701 98.67369
## 48.14286 89.13812 105.79777
## 48.28571 92.59844 109.84835
##
##
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 1.269089 1.269089 1.269089 1.269089 1.269089 1.269089 1.269089 1.269089
## [9] 1.269089 1.269089 1.269089 1.269089 1.269089 1.269089
##
## $佐賀県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 -0.7616789 -1.836703
## 46.57143 -0.8329319 -1.945675
## 46.71429 -0.9018475 -2.051072
## 46.85714 -0.9686417 -2.153225
## 47.00000 -1.0334991 -2.252416
## 47.14286 -1.0965790 -2.348888
## 47.28571 -1.1580201 -2.442854
## 47.42857 -1.2179438 -2.534499
## 47.57143 -1.2764572 -2.623988
## 47.71429 -1.3336555 -2.711465
## 47.85714 -1.3896235 -2.797061
## 48.00000 -1.4444374 -2.880892
## 48.14286 -1.4981658 -2.963062
## 48.28571 -1.5508707 -3.043667
##
## $佐賀県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 3.299857 4.374881
## 46.57143 3.371110 4.483853
## 46.71429 3.440026 4.589250
## 46.85714 3.506820 4.691403
## 47.00000 3.571677 4.790594
## 47.14286 3.634757 4.887066
## 47.28571 3.696198 4.981032
## 47.42857 3.756122 5.072678
## 47.57143 3.814636 5.162166
## 47.71429 3.871834 5.249644
## 47.85714 3.927802 5.335239
## 48.00000 3.982616 5.419070
## 48.14286 4.036344 5.501240
## 48.28571 4.089049 5.581846
##
##
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 2.705241 3.086921 2.649923 2.917747 2.827958 2.870411 3.558070 3.379086
## [9] 3.215713 3.177137 3.184646 3.168157 3.182070 3.513195
##
## $長崎県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 0.5960542 -0.5204820
## 46.57143 0.7381159 -0.5052662
## 46.71429 -0.1820238 -1.6811661
## 46.85714 -0.1993088 -1.8493783
## 47.00000 -0.6077529 -2.4265081
## 47.14286 -0.8292103 -2.7876715
## 47.28571 -0.4009075 -2.4966636
## 47.42857 -0.9685169 -3.2699987
## 47.57143 -1.4166711 -3.8689072
## 47.71429 -1.7572997 -4.3694330
## 47.85714 -2.0178852 -4.7719388
## 48.00000 -2.2972853 -5.1905156
## 48.14286 -2.5303040 -5.5542519
## 48.28571 -2.4377758 -5.5880294
##
## $長崎県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 4.814428 5.930965
## 46.57143 5.435725 6.679107
## 46.71429 5.481871 6.981013
## 46.85714 6.034802 7.684872
## 47.00000 6.263668 8.082423
## 47.14286 6.570032 8.528494
## 47.28571 7.517047 9.612803
## 47.42857 7.726688 10.028170
## 47.57143 7.848098 10.300334
## 47.71429 8.111575 10.723708
## 47.85714 8.387177 11.141230
## 48.00000 8.633599 11.526829
## 48.14286 8.894443 11.918391
## 48.28571 9.464166 12.614419
##
##
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 6.105131 7.855871 7.521560 7.401215 7.291283 7.190864 7.099135 7.015344
## [9] 6.938804 6.868887 6.805020 6.746680 6.693389 6.644709
##
## $熊本県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 1.78862222 -0.4963995
## 46.57143 2.29374316 -0.6506693
## 46.71429 1.86559909 -1.1284860
## 46.85714 1.46553081 -1.6766304
## 47.00000 1.12243847 -2.1431505
## 47.14286 0.82544021 -2.5442117
## 47.28571 0.56640674 -2.8918108
## 47.42857 0.33907147 -3.1951337
## 47.57143 0.13848767 -3.4613821
## 47.71429 -0.03932234 -3.6963073
## 47.85714 -0.19760449 -3.9045700
## 48.00000 -0.33903952 -4.0899929
## 48.14286 -0.46586359 -4.2557428
## 48.28571 -0.57995771 -4.4044651
##
## $熊本県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 10.42164 12.70666
## 46.57143 13.41800 16.36241
## 46.71429 13.17752 16.17161
## 46.85714 13.33690 16.47906
## 47.00000 13.46013 16.72572
## 47.14286 13.55629 16.92594
## 47.28571 13.63186 17.09008
## 47.42857 13.69162 17.22582
## 47.57143 13.73912 17.33899
## 47.71429 13.77710 17.43408
## 47.85714 13.80764 17.51461
## 48.00000 13.83240 17.58335
## 48.14286 13.85264 17.64252
## 48.28571 13.86938 17.69388
##
##
## $大分県
## $大分県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 8.714691 6.514040 7.538468 6.790136 7.038809 7.607903 7.470190 7.780977
## [9] 7.597248 7.450846 7.435754 7.321766 7.405767 7.444557
##
## $大分県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 6.980658 6.062716
## 46.57143 4.572068 3.544049
## 46.71429 5.288888 4.098032
## 46.85714 4.405657 3.143390
## 47.00000 4.636012 3.364047
## 47.14286 5.147260 3.844674
## 47.28571 4.976887 3.657013
## 47.42857 5.247088 3.905728
## 47.57143 4.975629 3.587827
## 47.71429 4.759422 3.334669
## 47.85714 4.658914 3.188944
## 48.00000 4.474784 2.967682
## 48.14286 4.503152 2.966600
## 48.28571 4.486200 2.920140
##
## $大分県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 10.448724 11.36667
## 46.57143 8.456012 9.48403
## 46.71429 9.788049 10.97890
## 46.85714 9.174615 10.43688
## 47.00000 9.441607 10.71357
## 47.14286 10.068546 11.37113
## 47.28571 9.963492 11.28337
## 47.42857 10.314865 11.65622
## 47.57143 10.218868 11.60667
## 47.71429 10.142270 11.56702
## 47.85714 10.212594 11.68256
## 48.00000 10.168748 11.67585
## 48.14286 10.308383 11.84493
## 48.28571 10.402914 11.96897
##
##
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 12.44477 14.20446 14.20446 14.20446 14.20446 14.20446 14.20446 14.20446
## [9] 14.20446 14.20446 14.20446 14.20446 14.20446 14.20446
##
## $宮崎県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 9.451937 7.867628
## 46.57143 11.032053 9.352681
## 46.71429 10.670707 8.800050
## 46.85714 10.343029 8.298909
## 47.00000 10.041061 7.837088
## 47.14286 9.759560 7.406570
## 47.28571 9.494855 7.001738
## 47.42857 9.244256 6.618480
## 47.57143 9.005723 6.253675
## 47.71429 8.777664 5.904890
## 47.85714 8.558811 5.570182
## 48.00000 8.348130 5.247974
## 48.14286 8.144770 4.936961
## 48.28571 7.948016 4.636052
##
## $宮崎県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 15.43760 17.02191
## 46.57143 17.37688 19.05625
## 46.71429 17.73822 19.60888
## 46.85714 18.06590 20.11002
## 47.00000 18.36787 20.57184
## 47.14286 18.64937 21.00236
## 47.28571 18.91407 21.40719
## 47.42857 19.16467 21.79045
## 47.57143 19.40321 22.15525
## 47.71429 19.63126 22.50404
## 47.85714 19.85012 22.83875
## 48.00000 20.06080 23.16095
## 48.14286 20.26416 23.47197
## 48.28571 20.46091 23.77288
##
##
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 7.528172 5.930011 5.360037 4.760049 4.760049 4.760049 4.760049 4.760049
## [9] 4.760049 4.760049 4.760049 4.760049 4.760049 4.760049
##
## $鹿児島県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 3.4420152 1.278934
## 46.57143 1.1152294 -1.433562
## 46.71429 0.3194411 -2.348889
## 46.85714 -0.3760554 -3.094945
## 47.00000 -0.3949382 -3.123823
## 47.14286 -0.4137521 -3.152597
## 47.28571 -0.4324978 -3.181266
## 47.42857 -0.4511761 -3.209832
## 47.57143 -0.4697877 -3.238296
## 47.71429 -0.4883332 -3.266659
## 47.85714 -0.5068135 -3.294922
## 48.00000 -0.5252292 -3.323086
## 48.14286 -0.5435809 -3.351153
## 48.28571 -0.5618693 -3.379123
##
## $鹿児島県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 11.614329 13.77741
## 46.57143 10.744793 13.29358
## 46.71429 10.400632 13.06896
## 46.85714 9.896153 12.61504
## 47.00000 9.915035 12.64392
## 47.14286 9.933849 12.67269
## 47.28571 9.952595 12.70136
## 47.42857 9.971273 12.72993
## 47.57143 9.989885 12.75839
## 47.71429 10.008430 12.78676
## 47.85714 10.026911 12.81502
## 48.00000 10.045326 12.84318
## 48.14286 10.063678 12.87125
## 48.28571 10.081967 12.89922
##
##
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## [1] 43.34728 38.96027 35.81941 36.16153 38.16638 49.92355 41.55012 42.33126
## [9] 42.33126 42.33126 42.33126 42.33126 42.33126 42.33126
##
## $沖縄県$lower
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 31.00378 24.469522
## 46.57143 25.61340 18.547995
## 46.71429 21.53951 13.980179
## 46.85714 21.00592 12.983022
## 47.00000 22.18296 13.721857
## 47.14286 33.15314 24.275429
## 47.28571 24.02804 14.752413
## 47.42857 22.85073 12.538359
## 47.57143 21.79801 10.928356
## 47.71429 20.79668 9.396965
## 47.85714 19.83990 7.933685
## 48.00000 18.92218 6.530162
## 48.14286 18.03911 5.179625
## 48.28571 17.18704 3.876489
##
## $沖縄県$upper
## Time Series:
## Start = c(46, 4)
## End = c(48, 3)
## Frequency = 7
## 80% 95%
## 46.42857 55.69078 62.22503
## 46.57143 52.30714 59.37254
## 46.71429 50.09932 57.65865
## 46.85714 51.31715 59.34005
## 47.00000 54.14979 62.61090
## 47.14286 66.69395 75.57166
## 47.28571 59.07220 68.34783
## 47.42857 61.81179 72.12416
## 47.57143 62.86452 73.73417
## 47.71429 63.86584 75.26556
## 47.85714 64.82263 76.72884
## 48.00000 65.74034 78.13236
## 48.14286 66.62341 79.48290
## 48.28571 67.47549 80.78603